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Asilomar Conference on Signals, Systems & Computers

作者： Y. Rua T.K. Sarkar Syracuse University Department of Electrical and Computer Engineering Syracuse University Syracuse NY USA

For the problem of estimating parameters of a linear system from its input and output sequences, we present iterative quadratic maximum likelihood (IQML), iterative quadratic weighted Kalman (IQWK), and noniterative subspace linear prediction (SLP) algorithms. The SLP algorithms are based on a novel subspace deconvolution of the output. In particular, a double total-least-squares (D-TLS) SLP algorithm is provided.

关键词： Kalman filters prediction algorithms Iterative algorithms Maximum likelihood estimation Iterative methods Least squares approximation Covariance matrix Contracts Parameter estimation Deconvolution

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Using prediction algorithms to Adjust the Frequency in Case of an Intermittent Synchronization Signal or Switching to Holdover Mode

Using Prediction Algorithms to Adjust the Frequency in Case ...

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Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

作者： A. S. Fen D. K. Mazurenko M. L. Schwartz Research Institute of Radio Moscow Russia Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798350373448

ISBN: (纸本)9798350373455

Precision Time Protocol (PTP) is a key candidate for delivering synchronization signals in packet switched networks. The use of clock signal rejection filters when transmitting a clock signal using the Precision Time Protocol (PTP) through traditional Ethernet packet communication networks leads to the need to use an intermittent clock signal in the frequency adjustment interval, a controlled oscillator (CO). In addition, it should be noted that the periodic transition of the CO into the holdover mode, associated with the loss of the clock signal, can also be regarded as a result of the occurrence of an intermittent clock signal. This article discusses issues related to the possibility of using prediction algorithms to ensure synchronous operation of the CO in the case of an intermittent synchronizing signal, as well as correction of the oscillator frequency drift in the holding mode

关键词： Time-frequency analysis Protocols Switches prediction algorithms Frequency measurement Telecommunications Synchronization

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Experimental Validation of the Performance of Channel prediction algorithms in MU-MIMO-OFDM Downlink System

Experimental Validation of the Performance of Channel Predic...

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IEEE Vehicular Technology Conference

作者： Maneesha Sharma Dhammika Jayalath Hajime Suzuki Chang Kyung Sung Science and Engineering Faculty Queensland University of Technology Brisbane Australia Data61 CSIRO Sydney Australia

In multi-user multiple input multiple output orthogonal frequency division multiplexing (MU-MIMO-OFDM) downlink system, it is important to have accurate channel state information (CSI) at the base station (BS) in order to minimize the inter-user interference (IUI). However when the channel changes rapidly, the available CSI at the BS can be outdated and this leads to the performance loss. The channel prediction algorithms can be used to predict the future CSI and compensate for the discrepancy between the estimated channel and the actual channel, and improve the performance of the overall system. In this paper, we propose to use recursive least square (RLS) algorithm to predict the CSI for the upcoming frame in MU-MIMO-OFDM downlink system. The simulation results show that RLS algorithm outperforms the other prediction algorithms such as normalized least mean square (NLMS) and the conventional linear extrapolation method. The simulation results are further validated with the measured channel results.

关键词： Channel estimation Downlink prediction algorithms Antenna measurements Precoding OFDM Transmitting antennas

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prediction and Retrodiction algorithms for Path-Constrained Targets

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2014年第4期50卷 2746-2761页

作者： Krishanth, Krishnan Tharmarasa, Ratnasingham Kirubarajan, Thiagalingam Valin, Pierre Meger, Eric McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada Def Res & Dev Canada Hamilton ON Canada ExactEarth Ltd Cambridge ON Canada

This paper presents algorithms for prediction and retrodiction of targets whose motion is constrained by external conditions (e.g., shipping lanes, roads). The targets are moving along paths defined by waypoints and segments. Measurements are obtained by sensors (e.g., spaceborne) at low revisit rates. Existing tracking algorithms assume that the targets follow the same motion model between successive measurements, but in a low revisit rate scenario targets may change the motion model between successive measurements. The proposed prediction algorithm addresses this issue by considering the possible changes in the motion model whenever targets move to a different segment. Further, when a target approaches an intersection, it has the possibility to travel into one of the multiple segments beyond that intersection. To predict the probable locations, multiple hypotheses for segments are introduced and a probability is calculated for each segment hypothesis. When measurements become available later, segment hypothesis probabilities are updated based on a combined mode likelihood and a sequential probability ratio test (SPRT) is carried out to reject the unlikely hypotheses. Retrodiction for path-constrained targets is also considered, because in some scenarios it is desirable to find out a target's location at some previous time (e.g., at the time of an accident) given all subsequent measurements. A retrodiction algorithm is also developed for path-constrained targets so as to facilitate motion forensic analysis. Simulation results and a performance measure for path-constrained target tracking are presented to validate the proposed algorithms.

关键词： Adaptation models Motion segmentation prediction algorithms Sensors Smoothing methods Target tracking Time measurement

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Circular prediction algorithms-hybrid filters

Circular prediction algorithms-hybrid filters

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American Control Conference (ACC)

作者： D. Tenne T. Singh Department of Mechanical & Aerospace Engineering State University of New York Buffalo USA

The focus of the paper is on the development of a geometric algorithm as an alternative to the coordinated turn procedures. The fact that the trajectories of maneuvering targets are always smooth has been exploited to derive an algorithm, which constrains the predicted position of the target to lie on a circle formed by prior measurements. This filter is then used in conjunction with an /spl alpha/-/spl beta/ filter to arrive at a series of filters, which are tested on benchmark trajectories. The performances show promising results for the proposed filters.

关键词： prediction algorithms Filters Acceleration Predictive models Aircraft Kinematics Aerodynamics Nonlinear equations Aerospace engineering Position measurement

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Missing Data Imputation in Bridge Monitoring System Based on the prediction algorithms

Missing Data Imputation in Bridge Monitoring System Based on...

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Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International Conference on

作者： Jiaqi Xu School of Civil Engineering Tongji University of China Shanghai China

In this paper, combined with the popular prediction model in the field of artificial intelligence, experimental simulation and prediction of missing data of bridge health monitoring are carried out. The prediction models involved include support vector regression model SVM, neural network prediction model, Gaussian process regression model and time series prediction model. Firstly, by introducing the background and research status of BSHMS, the existing problems in the literature and the core work of this paper are proposed. Secondly, this paper introduces the basic principles of the related algorithms, which lays a foundation for the later model building. Third, the three groups of data were tested for normal distribution by the KPSS test method, and it was decided to use 3σ rule in order to identify abnormal data,while box chart rule is used for S2 and S3 measuring points. Then, the super parameters of the four models are optimized through several experiments, and the time series prediction model is simplified to AR model by using the characteristics of ACF and PACF images. Finally, the four models are simulated and the results are compared. The results show that SVR, BP and GPR all have good prediction effects, especially BP neural network, but the relative deviation of the prediction results of AR model is large, which may be due to the fact that only the impact of time series is considered in this case, and the limitation of reference data is not considered.

关键词： Support vector machines Bridges Time series analysis Neural networks Predictive models prediction algorithms Data models

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Comparative Study on Different Wind Power prediction algorithms Based on Comprehensive Performance Evaluation

Comparative Study on Different Wind Power Prediction Algorit...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Ke Han Limengqian Zheng Yuanyi Bao Yangwu Shen Chang Liu Lipeng Zhu Yuchen Huang Yongjin Yu CTG Wuhan Science and Technology Innovation Park China Three Gorges Corporation Wuhan China College of Electrical and Information Engineering Hunan University Changsha China POWERCHINA Chengdu Engineering Corporation Limited Chengdu China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

Considering that existing studies on wind power prediction performance evaluation struggle to fully capture the prediction performance, this paper constructs a comprehensive evaluation scheme that incorporates fundamental indices, long time-scale indices, and key time-period indices. Subsequently, weights are automatically assigned to these indices through the entropy weighting method, and the weighted sum is calculated to derive the final comprehensive score for each prediction result, enabling a scientific and comprehensive evaluation of wind power prediction performance across various time scales. Comparative studies are conducted using measured data from a wind farm in China and predicted power data from various vendors. The results demonstrate that the comprehensive evaluation scheme enables a scientific and holistic assessment of wind farm power prediction results. Furthermore, an analysis of the error characteristics is conducted, revealing potential limitations of the wind power prediction algorithms deployed by actual vendors.

关键词： Performance evaluation Power measurement Error analysis Wind power generation Wind farms prediction algorithms Entropy Power systems

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Utilizing Genomic prediction algorithms for Enhancing Resistance to Fungal Root Rot in Vertical Soilless Farming

Utilizing Genomic Prediction Algorithms for Enhancing Resist...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： T. Prabahar Godwin James K. Bala Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Bharath Institute of Higher Education and Research Chennai Tamil Nadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

The goal of this project is to improve the resistance of crops to fungal root rot diseases, including Fusarium, Rhizoctonia, and Pythium within vertical soilless farming systems using genomic prediction approaches. Beside merging of the phenotypic and genetic data the study aims at discovering relevant gene markers that are responsible for disease resistance. Molecular biology and bioinformatics approaches shall be used to design decision support systems that will predict the vulnerability of different cultivars to root rot infection given environmental and genetic factors. Vertical soilless farming imposes specific risks and opportunities for disease control and the management of crop disease requires accurate data *** investigation will contribute to better understanding the genetic variability of resistance-related genes in the target pathogens, as well as help to identify proper, genetically adapted for the high-stress conditions. Lastly, the purpose of the findings is to enhance the stability and productivity of crop yield and food security under conditions of limited soil availability and other aspects of soilless systems. This work therefore provides a call for adopting the best breeding practices combined with modern genomic tools for developing stronger agricultural systems that will be able to withstand biotic stresses.

关键词： Accuracy Genomics Food security Machine learning Soil prediction algorithms Bioinformatics Immune system Diseases Farming

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Occlusion prediction algorithms for multi-camera network

Occlusion prediction algorithms for multi-camera network

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Rahul Raman Pankaj K. Sa Banshidhar Majhi Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha India

The mode of object tracking has evolved from single camera tracking to multi-camera tracking over the last few years. Even though multi-camera model overcomes limitations present in single camera system, it introduces complexities of handling network of multiple cameras. In this paper, we propose a novel real time occlusion prediction method for multi-camera network thus reducing complexities and cost of tracking in multi-camera model without losing track of the subject. The proposed method constitutes sequential phases as (a) estimation of direction of relative motion in real plane based on change in bounding pixel positions of tracked subject, (b) constructing algorithms for occlusion prediction, and (c) mitigation of occlusion by awaking a minimal subset of cameras in network. The method uses the pattern of change in the dimensions of bounding box with respect to frame number, obtained by applying background subtraction to the video of mutual motion of the subjects. On the basis of estimated direction of motion it uses proposed algorithms to decide the possibility and proximity of occlusion and thus awaking the minimal set of camera in the network that does not encounter occlusion. The proposed approach has been verified using various video samples and is observed that the proposed method successfully predicts the occurrence of occlusion.

关键词： Cameras prediction algorithms Vectors Algorithm design and analysis Complexity theory Object tracking

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Simulation study of a class of autonomous host-centric mobility prediction algorithms for wireless cellular and ad hoc networks

Simulation study of a class of autonomous host-centric mobil...

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Annual Symposium on Simulation

作者： Ing-Ray Chen N. Verma Department of Computer Science Virginia Technology USA

We propose and analyze a class of autonomous host-centric mobility prediction algorithms via simulation for predicting future movements of mobile hosts based on recent and past movement histories observed. These mobility prediction algorithms are 'autonomous' and 'host-centric' since they require individual mobile hosts, rather than fixed base stations, to collect and maintain mobility prediction data, thus making them applicable to both cellular and ad hoc wireless networks. Each mobile host under these autonomous host-centric mobility prediction algorithms is capable of predicting the probability that it will leave the current location to the next location within a time period, thus facilitating resource reservation and route maintenance decisions, as well as for estimating the mobile host's residence time. We describe a simulation environment for evaluating the prediction accuracy for this probability metric by these algorithms, and present the evaluation results with physical interpretations and their applicability given.

关键词： Predictive models prediction algorithms Cellular networks Ad hoc networks Algorithm design and analysis Analytical models History Base stations Wireless networks Accuracy

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